Learning a Style Space for Interactive Line Drawing Synthesis from Animated 3D Models
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Date
2022
Authors
Journal Title
Journal ISSN
Volume Title
Publisher
The Eurographics Association
Abstract
Most non-photorealistic rendering (NPR) methods for line drawing synthesis operate on a static shape. They are not tailored to process animated 3D models due to extensive per-frame parameter tuning needed to achieve the intended look and natural transition. This paper introduces a framework for interactive line drawing synthesis from animated 3D models based on a learned style space for drawing representation and interpolation. We refer to style as the relationship between stroke placement in a line drawing and its corresponding geometric properties. Starting from a given sequence of an animated 3D character, a user creates drawings for a set of keyframes. Our system embeds the raster drawings into a latent style space after they are disentangled from the underlying geometry. By traversing the latent space, our system enables a smooth transition between the input keyframes. The user may also edit, add, or remove the keyframes interactively, similar to a typical keyframe-based workflow. We implement our system with deep neural networks trained on synthetic line drawings produced by a combination of NPR methods. Our drawing-specific supervision and optimization-based embedding mechanism allow generalization from NPR line drawings to user-created drawings during run time. Experiments show that our approach generates high-quality line drawing animations while allowing interactive control of the drawing style across frames.
Description
CCS Concepts: Computing methodologies → Non-photorealistic rendering; Animation; Learning latent representations
@inproceedings{10.2312:pg.20221237,
booktitle = {Pacific Graphics Short Papers, Posters, and Work-in-Progress Papers},
editor = {Yang, Yin and Parakkat, Amal D. and Deng, Bailin and Noh, Seung-Tak},
title = {{Learning a Style Space for Interactive Line Drawing Synthesis from Animated 3D Models}},
author = {Wang, Zeyu and Wang, Tuanfeng Y. and Dorsey, Julie},
year = {2022},
publisher = {The Eurographics Association},
ISBN = {978-3-03868-190-8},
DOI = {10.2312/pg.20221237}
}